Lung Imaging based Risk Score (LunIRIS): Decision support tool for screening CT

基于肺部影像的风险评分 (LunIRIS)：筛查 CT 的决策支持工具

• 批准号: 10427198
• 负责人: Anant Madabhushi
• 金额: --
• 依托单位: LOUIS STOKES CLEVELAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10427198
• 关键词: 3-Dimensional; Adopted; Anxiety; Architecture; Area; Benign; Biopsy; Blood Vessels; Bronchoscopy; Cancerous; Carcinogens; Chest; Clinical; Clinical Trials; Clinics and Hospitals; Computer software; Computer-Assisted Diagnosis; Data; Detection; Diagnosis; Diagnostic; Discrimination; Dose; Evaluation; Excision; Exposure to; General Population; Glean; Health Promotion; Human; Image; Image Analysis; Lung; Lung CAT Scan; Lung nodule; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medicare; Morbidity - disease rate; Morphology; Nodule; Operative Surgical Procedures; Outcome; Patients; Pattern; Pattern Recognition; Phenotype; ROC Curve; Reader; Research Personnel; Risk; Role; Scanning; Shapes; Smoke; Smoking; Smoking History; Specificity; Technology; Texture; United States Department of Veterans Affairs; University Hospitals; Validation; Veterans; Visual; X-Ray Computed Tomography; base; care systems; chest computed tomography; cohort; computed tomography screening; computerized; cost; diagnostic accuracy; disorder prevention; follow-up; imaging biomarker; improved; innovation; lung cancer screening; lung imaging; machine learning classifier; male; military service; military veteran; novel; programs; radiologist; radiomics; routine screening; screening; success; support tools; tool; tumor

ABSTRACT: Recent data from the National Lung Screening Trial (NLST) suggest that annual low-dose chest CT scans in patients who smoke, leads to early detection of lung cancer and improves survival. CMS/Medicare has consequently approved CT scans for lung cancer screening, and the VA National Center for Health Promotion and Disease Prevention has adopted a similar approach. The Veteran (VA) population is at increased risk of developing lung cancer as compared to the general population because of higher smoking rates and increased likelihood of exposure to other carcinogens during their military service. The VA system cares for some 6.7 million mostly older male veterans each year, many of whom have long smoking histories. In a recent study, investigators from eight VA centers across the U.S. screened more than 2,000 Veterans over two years using criteria from the NLST. Among the 2,106 Veterans screened, a total of 1,257 (59.7%) had nodules, of which 1,184 (56.2%) required tracking. Nearly all of the positive results were negative for cancer, producing a false- positive rate of 97.5% for human-based interpretation. In the general population, many of the lung nodules identified by human readers as “indeterminate” or “suspicious” on chest CT trigger additional surgical interventions (~$5K-$25K/patient) and CT exams, but >30% of these nodules on subsequent biopsies or resection are identified as being benign. The current low false positive rate in diagnosis of nodules on screening CT exams results in patient anxiety, and one of the reasons for poor compliance in lung cancer screening. As a result, there is an urgent need for better image based decision support tools for improving lung cancer screening. PI Anant Madabhushi and his team have developed novel computerized image analysis and pattern recognition tools for improved discrimination of cancerous from non-cancerous nodules on routine screening chest CT scans. A significant breakthrough has been in developing a novel imaging marker called “vessel tortuosity” for quantitatively characterizing the architectural complexity of the vasculature of a lung nodule on chest CT scans; measurements of vessel tortuosity being significantly different between benign and malignant lung nodules. Additionally our group has also identified other highly predictive image features that aim to capture (1) subtle textural patterns of the microarchitecture within and immediately outside the nodule, and (2) subtle 3D shape patterns of the nodule. Each of these imaging markers has been independently shown to have an area under the receiver operating characteristic curve (AUC) ranging from 77-87% in distinguishing malignant from benign nodules in a validation set of N=145 patients. By contrast, on this cohort an expert chest radiologist and pulmonologist had a maximum AUCs of 69-72%. More interestingly, on this cohort combining machine based interpretations with human readers resulted in an improvement of 30% in the AUC value for the human readers. Building on our current impressive results, in this study we propose to continue to optimize our computerized decision support technology (Lung Imaging based Risk Score (LunIRiS)) to assign a risk score of malignancy to a nodule on a chest CT scan. In Aim 1 we will identify the best combination of intra- and peri- nodule texture, 3D shape, margin sharpness and vessel tortuosity measurements for constructing the LunIRiS software program by employing a cohort of over N=300 patients. In Aim 2, LunIRiS will be independently validated on N=300 retrospective cases from the Cleveland VA. We will then deploy the LunIRiS program at the Cleveland VA in Aim 3 to quantitatively evaluate its role as a decision support tool. On an independent cohort of N=250 CT screening exams from Veteran patients, radiologists and pulmonologists at the Cleveland VA will first independently read the scans; following a wash out period they will perform a second interpretation with LunIRiS. Interpretation results with and without LunIRiS will then be compared to evaluate additional benefit of LunIRiS.

摘要：国家肺筛查试验（NLST）的最新数据表明，每年低剂量胸部 吸烟患者的CT扫描可以早期发现肺癌并提高生存率。CMS/Medicare 因此批准了CT扫描用于肺癌筛查，VA国家卫生中心 促进和疾病预防局也采取了类似的做法。退伍军人（VA）人口增加 与普通人群相比，由于吸烟率较高， 在服兵役期间暴露于其他致癌物质的可能性增加。VA系统关心一些 6.7每年有100万人，其中大多数是老年男性退伍军人，其中许多人有长期吸烟史。在最近的一项研究中， 来自美国八个退伍军人事务中心的调查人员在两年多的时间里使用 NLST的标准。在接受筛查的2,106名退伍军人中，共有1,257人（59.7%）患有结节，其中 1，184（56.2%）需要跟踪。几乎所有的阳性结果都是阴性的，产生了一个错误的- 人工判读阳性率为97.5%。在一般人群中，许多肺结节 人类阅片员在胸部CT上识别为“不确定”或“可疑”， 介入治疗（约5000 - 25000美元/患者）和CT检查，但>30%的这些结节在随后的活检中或 切除被确定为良性。目前筛查诊断结节的假阳性率较低 CT检查导致患者焦虑，也是肺癌筛查依从性差的原因之一。作为 因此，迫切需要更好的基于图像的决策支持工具来改善肺癌筛查。 PI Anant Madabhushi和他的团队开发了新颖的计算机图像分析和模式 用于改进常规筛查中癌性结节与非癌性结节的区分的识别工具 胸部CT扫描一个重要的突破是开发了一种称为“血管”的新型成像标记物 用于定量地表征肺结节的脉管系统的结构复杂性， 胸部CT扫描;良性和恶性之间血管迂曲度的测量值存在显著差异 肺结节此外，我们的团队还确定了其他高度预测的图像特征，旨在 捕获（1）结节内部和外部微结构的细微纹理模式，以及（2） 结节的精细3D形状模式。这些成像标记中的每一个都被独立地显示为 具有在区分受试者操作特征曲线下面积（AUC）范围为77-87%， 在N=145名患者的验证集中，从良性结节到恶性结节。相比之下，在这个队列中， 放射科医师和肺病科医师的最大AUC为69- 72%。更有趣的是，在这组人群中， 与人类读者进行基于机器的解释，使其AUC值提高了30% 人类读者 基于我们目前令人印象深刻的结果，在这项研究中，我们建议继续优化我们的 计算机化决策支持技术（基于肺部成像的风险评分（LunIRiS））， 从恶性肿瘤到胸部CT扫描的结节在目标1中，我们将确定内部和外部的最佳组合， 用于构建LunIRiS的结节纹理、3D形状、边缘清晰度和血管迂曲度测量 通过采用超过N=300名患者的队列对软件编程。在目标2中，LunIRiS将独立于 在克利夫兰VA的N=300例回顾性病例中进行了验证。然后，我们将部署LunIRiS计划， Cleveland VA在Aim 3中定量评估其作为决策支持工具的作用。在一个独立的队列中， N=250例来自Cleveland VA的退伍军人患者、放射科医生和肺病科医生的CT筛查检查将 首先独立读取扫描;在洗脱期后，他们将进行第二次解读， LunIRiS。然后将比较使用和不使用LunIRiS的判读结果，以评价 LunIRiS。